Update app.py

app.py

@@ -1207,7 +1207,7 @@ def answer_question(image, question):
 with gr.Blocks() as demo:
     gr.HTML("<h1 class='gradio-heading'><center>🌔 moondream1</center></h1>")
     gr.HTML(
-        "<center><p class='gradio-sub-heading'>moondream1 is an older version of the moondream model. Check out the <a href='https://huggingface.co/spaces/vikhyatk/moondream2'></a> space for an improved version.</p></center>"
+        "<center><p class='gradio-sub-heading'>moondream1 is an older version of the moondream model. Check out the <a href='https://huggingface.co/spaces/vikhyatk/moondream2'>moondream2</a> space for an improved version.</p></center>"
     )
     with gr.Group():
         with gr.Row():
@@ -1228,24 +1228,3 @@ with gr.Blocks() as demo:
 
 demo.queue().launch(debug=True)
 
-# gr.Interface(
-#     title="🌔 moondream1",
-#     description="""
-#         moondream1 is a tiny (1.6B parameter) vision language model trained by <a href="https://x.com/vikhyatk">@vikhyatk</a> that performs on par with models twice its size. It is trained on the LLaVa training dataset, and initialized with SigLIP as the vision tower and Phi-1.5 as the text encoder.  Check out the <a href="https://huggingface.co/vikhyatk/moondream1">HuggingFace model card</a> for more details.
-#     """,
-#     fn=answer_question,
-#     inputs=[gr.Image(type="pil"), gr.Textbox(lines=2, label="Question")],
-#     examples=[
-#         [Image.open("assets/demo-1.jpg"), "Who is the author of this book?"],
-#         [Image.open("assets/demo-2.jpg"), "What type of food is the girl eating?"],
-#         [
-#             Image.open("assets/demo-3.jpg"),
-#             "What kind of public transportation is in the image?",
-#         ],
-#         [Image.open("assets/demo-4.jpg"), "What is the girl looking at?"],
-#         [Image.open("assets/demo-5.jpg"), "What kind of dog is in the picture?"],
-#     ],
-#     outputs=gr.TextArea(label="Answer"),
-#     allow_flagging="never",
-#     cache_examples=False,
-# ).launch()